Melanoma skin cancer accounts for less than 5 percent of skin cancer cases but causes the most skin-cancer related deaths. Convenient, automated diagnosis of skin lesions and melanoma recognition can greatly improve early detection of melanomas.
"A Mobile Automated Skin Lesion Classification System," a paper presented by researchers from the University of Missouri at the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, describes a prototype of an image-based automated melanoma recognition system for Android smart phones. The system consists of three major components: image segmentation, feature calculation, and classification. It is designed to run on a mobile device with a camera, such as a smart phone or a tablet PC. A skin lesion image is converted to a monochrome image for outline contour detection. Color and shape features of the lesion are extracted and used as input to a kNN classifier. Initial experimental results show that the system is efficient and works well on properly lighted test images, achieving an average accuracy of 66.7 percent.
Papers from ICTAI 2011 are available to both IEEE Computer Society members and paid subscribers via the Computer Society Digital Library.